https://github.com/compcode1/advanced-data-engineering
Implementing advanced data engineering techniques, including dictionaries, sets, and binary search, to achieve significant improvements in time and memory efficiency.
https://github.com/compcode1/advanced-data-engineering
algorithms-and-data-structures efficiency
Last synced: 6 months ago
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Implementing advanced data engineering techniques, including dictionaries, sets, and binary search, to achieve significant improvements in time and memory efficiency.
- Host: GitHub
- URL: https://github.com/compcode1/advanced-data-engineering
- Owner: Compcode1
- License: mit
- Created: 2024-06-30T15:43:21.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-30T17:19:21.000Z (over 1 year ago)
- Last Synced: 2025-03-18T01:15:54.784Z (10 months ago)
- Topics: algorithms-and-data-structures, efficiency
- Language: Jupyter Notebook
- Homepage:
- Size: 14.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# advanced-data-engineering
"Implementing advanced data engineering techniques, including dictionaries, sets, and binary search, to achieve significant improvements in time and memory efficiency."
# Advanced Data Engineering
## Introduction
The main objective of this project is to demonstrate my ability to understand and implement advanced data engineering techniques to optimize both time and memory efficiency. Leveraging efficient data structures and algorithms is crucial for handling large datasets effectively and efficiently. The techniques I employed in this project include the use of dictionaries for fast lookups, sets for quick membership testing, and binary search for efficient range queries. These methods ensure that data retrieval and processing operations are performed swiftly, reducing overall computational time and memory usage.
In today's data-driven world, time and memory efficiency are vital. Fast data processing enables quicker insights and decision-making, helping businesses stay competitive. Memory-efficient algorithms ensure large datasets are manageable, preventing system overloads. These optimizations are key to advancing data technologies and applications.
I worked with a dataset from a fictitious online laptop store and built algorithms dedicated to efficient solutions for various business questions about our inventory. I created a class representing the inventory, and the methods within that class handled the queries and produced the results. This included preprocessing the data to make those queries time and memory optimized. The dataset used for this project can be accessed at [Kaggle - Laptop Prices](https://www.kaggle.com/datasets/muhammetvarl/laptop-price) (the IDs have been changed and the prices were converted to integers).
## Conclusion
The main objective of this project was to demonstrate the ability to understand and implement advanced data engineering techniques to optimize both time and memory efficiency. By leveraging efficient data structures and algorithms such as dictionaries, sets, and binary search, the project aimed to handle large datasets effectively and efficiently. These techniques ensured swift data retrieval and processing operations, significantly reducing computational time and memory usage.
The results of the project clearly show significant improvements in performance. The optimized dictionary-based lookup method was approximately 253 times faster than the standard method, and the set-based promotion check method was approximately 2,562 times faster than its standard counterpart. Additionally, the binary search implementation for budget-based queries effectively identified the first laptop exceeding a given price, confirming its utility and efficiency. Overall, the project successfully highlighted the substantial benefits of using advanced data engineering techniques for large-scale data handling.
In today's era of big data and AI, time and memory efficiency are crucial. Efficient data processing allows for faster insights and decision-making, enabling businesses to stay competitive and innovative. Memory-efficient algorithms ensure that even large datasets can be managed and analyzed without overwhelming system resources. These optimizations are fundamental in advancing the capabilities of data-driven technologies and applications.
## Project Contents
- `notebook.ipynb`: The main Jupyter Notebook with the project code and analysis.
- `data/`: Directory containing the dataset used in the project.
- `README.md`: This file.
## How to Use
### Cloning the Repository
1. **Open Terminal (or Command Prompt)** on your local machine.
2. **Clone the repository**:
```bash
git clone https://github.com/your-username/advanced-data-engineering.git
cd advanced-data-engineering